Modeling Heterogeneous Treatment effects in large scale experiments using bayesian additive regression trees

  • Green D
  • Kern H
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We present a methodology that largely automates the search for systematic
treatment effect heterogeneity in large-scale experiments. We introduce a nonparametric
estimator developed in statistical learning, Bayesian Additive Regression Trees (BART), to
model treatment effects that vary as a function of covariates. BART has several advantages
over commonly employed parametric modeling strategies, in particular its ability to automatically
detect and model relevant treatment-covariate interactions in a flexible manner.
To increase the reliability and credibility of the resulting conditional treatment effect estimates,
we suggest the use of a split sample design. The data are randomly divided into two
equally-sized parts, with the first part used to explore treatment effect heterogeneity and
the second part used to confirm the results. This approach permits a relatively unstructured
data-driven exploration of treatment effect heterogeneity while avoiding charges of
data dredging and mitigating multiple comparison problems. We illustrate the value of our
approach by offering two empirical examples, a survey experiment on Americans support
for social welfare spending and a voter mobilization field experiment. In both applications,
BART provides robust insights into the nature of systematic treatment effect heterogeneity.

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  • D Green

  • H Kern

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